





"""simple docstring"""





from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=1e-12     )   ->Tuple:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				jnp.divide(emb_a.T	,  jnp.clip(jnp.linalg.norm(_SCREAMING_SNAKE_CASE	,  axis=1     )	,  a_min=_SCREAMING_SNAKE_CASE     )     ).T
  lowerCAmelCase__     :Any											=				jnp.divide(emb_a.T	,  jnp.clip(jnp.linalg.norm(_SCREAMING_SNAKE_CASE	,  axis=1     )	,  a_min=_SCREAMING_SNAKE_CASE     )     ).T
  return jnp.matmul(_SCREAMING_SNAKE_CASE	,  norm_emb_a.T     )






class    _lowerCAmelCase  (	nn.Module			):
       """simple docstring"""




       __magic_name__	:CLIPConfig
       __magic_name__	:jnp.dtype						    =   jnp.floataa






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				FlaxCLIPVisionModule(self.config.vision_config )
         lowerCAmelCase__     :str											=				nn.Dense(self.config.projection_dim       ,					use_bias=__UpperCAmelCase       ,					dtype=self.dtype )

         lowerCAmelCase__     :Tuple											=				self.param('concept_embeds'       ,					jax.nn.initializers.ones       ,					(1_7, self.config.projection_dim) )
         lowerCAmelCase__     :int											=				self.param(
             'special_care_embeds'       ,					jax.nn.initializers.ones       ,					(3, self.config.projection_dim) )

         lowerCAmelCase__     :Any											=				self.param('concept_embeds_weights'       ,					jax.nn.initializers.ones       ,					(1_7,) )
         lowerCAmelCase__     :str											=				self.param('special_care_embeds_weights'       ,					jax.nn.initializers.ones       ,					(3,) )






       def __call__(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				self.vision_model(__UpperCAmelCase )[1]
         lowerCAmelCase__     :int											=				self.visual_projection(__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				jax_cosine_distance(__UpperCAmelCase       ,					self.special_care_embeds )
         lowerCAmelCase__     :Dict											=				jax_cosine_distance(__UpperCAmelCase       ,					self.concept_embeds )

         # increase this value to create a stronger `nfsw` filter
         # at the cost of increasing the possibility of filtering benign image inputs
         lowerCAmelCase__     :str											=				0.0

         lowerCAmelCase__     :Dict											=				special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
         lowerCAmelCase__     :Optional[int]											=				jnp.round(__UpperCAmelCase       ,					3 )
         lowerCAmelCase__     :List[str]											=				jnp.any(special_scores > 0       ,					axis=1       ,					keepdims=__UpperCAmelCase )
         # Use a lower threshold if an image has any special care concept
         lowerCAmelCase__     :Tuple											=				is_special_care * 0.01

         lowerCAmelCase__     :Tuple											=				cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
         lowerCAmelCase__     :Tuple											=				jnp.round(__UpperCAmelCase       ,					3 )
         lowerCAmelCase__     :Tuple											=				jnp.any(concept_scores > 0       ,					axis=1 )

         return has_nsfw_concepts






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Optional[int]						    =   CLIPConfig
       __magic_name__	:Any						    =   """clip_input"""
       __magic_name__	:Tuple						    =   FlaxStableDiffusionSafetyCheckerModule






       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 0       ,					__UpperCAmelCase = jnp.floataa       ,					__UpperCAmelCase = True       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         if input_shape is None:
           lowerCAmelCase__     :Dict											=				(1, 2_2_4, 2_2_4, 3)
         lowerCAmelCase__     :Optional[int]											=				self.module_class(config=__UpperCAmelCase       ,					dtype=__UpperCAmelCase       ,					**__UpperCAmelCase )
         super().__init__(__UpperCAmelCase       ,					__UpperCAmelCase       ,					input_shape=__UpperCAmelCase       ,					seed=__UpperCAmelCase       ,					dtype=__UpperCAmelCase       ,					_do_init=_do_init )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				jax.random.normal(__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[Any]											=				jax.random.split(__UpperCAmelCase )
         lowerCAmelCase__     :Union[str, Any]											=				{'params': params_rng, 'dropout': dropout_rng}

         lowerCAmelCase__     :Optional[int]											=				self.module.init(__UpperCAmelCase       ,					__UpperCAmelCase )['params']

         return random_params






       def __call__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				jnp.transpose(__UpperCAmelCase       ,					(0, 2, 3, 1) )

         return self.module.apply(
             {'params': params or self.params}       ,					jnp.array(__UpperCAmelCase       ,					dtype=jnp.floataa )       ,					rngs={}       ,					)








"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")